Sensing and computing control system for shaping precise temporal physical states

ABSTRACT

According to some embodiments, system and methods are provided, comprising an installed product, including a plurality of components; a computer programmed with a damage metric model for the installed product, the damage metric model for providing an estimate of an extent of damage on one or more components; the computer programmed with a dynamic process control model for providing a dynamic response of the installed product with respect to its one or more operating parameters; the computer further programmed with a true-up model for providing a control action to reduce an uncertainty of the estimate provided by the damage metric model; the computer including a processor and a memory in communication with the processor, the memory storing the damage metric model and the true-up model; the memory storing additional program instructions, the processor operative with the additional program instructions to perform functions as follows: receiving an estimate output of the damage metric model, wherein the output includes the estimate of the extent of damage on the one or more components; generating, via the dynamic process control model, an operating response of the installed product to the received estimate output; in response to receipt of the estimate output, executing the true-up model; and generating, via execution of the true-up model, the plan to reduce uncertainty of the estimate output. Numerous other aspects are provided.

BACKGROUND

Industrial equipment or assets, generally, are engineered to performparticular tasks as part of industrial processes. For example,industrial assets can include, among other things and withoutlimitation, manufacturing equipment on a production line, aircraftengines, wind turbines that generate electricity on a wind farm, powerplants, locomotives, healthcare or imaging devices (e.g., X-ray or MRIsystems) for use in patient care facilities, or drilling equipment foruse in mining operations. The design and implementation of these assetsoften takes into account both the physics of the task at hand, as wellas the environment in which such assets are configured to operate andthe specific operating control these systems are assigned to.

Low-level software and hardware-based controllers have long been used todrive industrial assets. However, the rise of inexpensive cloudcomputing, increasing sensor capabilities, and decreasing sensor costs,as well as the proliferation of mobile technologies and cloud computingwith services oriented architectures have created opportunities forcreating novel industrial assets with improved sensing and controltechnology that are capable of transmitting data that can then betransmitted to a network. As a consequence, there are new opportunitiesto enhance the performance value of complex industrial asset basedsystems using integrated industrial-focused hardware and computingcontrol software.

Assets, including the asset components, typically acquire damage duringassigned operations. For industries, a challenge is assessing a healthstatus (e.g., state of damage) of an asset at a one or more points intime, and then scheduling appropriate maintenance or dynamicallychanging the duty assignment or maintenance to avoid unexpected failuresor reduction in operational utility. Conventionally, a mix of sensorsand analytics in condition-based and prognostic maintenance programsprovide a simple threshold level for a specific asset such as an enginecomponent, without any regard to system-level impacts and have no meansto control the physical states of the assets or the processes which usethem.

It would be desirable to provide systems and methods to improveoptimized maintenance and operational control interventions of anintegrated sensing and control industrial asset.

BRIEF DESCRIPTION

According to some embodiments, a system includes providing an installedproduct, including a plurality of components; a computer programmed witha damage metric model for the installed product, the damage metric modelfor providing an estimate of an extent of damage on one or morecomponents; the computer programmed with a dynamic process control modelfor providing the dynamic response of the physical system with respectto its one or more operating parameters; the computer further programmedwith a true-up model for providing a control action to reduce anuncertainty of the estimate provided by the damage metric model; thecomputer including a processor and a memory in communication with theprocessor, the memory storing the damage metric model and the true-upmodel; the memory storing additional program instructions, the processoroperative with the additional program instructions to perform functionsas follows: receiving an estimate output of the damage metric model,wherein the output includes the estimate of the extent of damage on theone or more components; generating, via the dynamic process controlmodel, an operating response of the installed product to the receivedestimate output; in response to receipt of the estimate output,executing the true-up model; and generating, via execution of thetrue-up model, the control action to reduce uncertainty of the estimateoutput.

According to some embodiments, a method includes receiving, at a damagemetric model, state data associated with one or more components of aninstalled product; generating, via the damage metric model, an estimateoutput of an extent of damage the on one or more components; receivingthe estimate output at a damage module; generating, via a dynamicprocess control model, an operating response of the installed product tothe received estimate output; and in response to receiving the estimateoutput, executing a true-up model to generate a control action to reduceuncertainty of the estimate output.

According to some embodiments, a non-transitory, computer-readablemedium storing instructions that, when executed by a computer processor,cause the computer processor to perform a method comprising: receiving,at a damage metric model, state data associated with one or morecomponents of an installed product; generating, via the damage metricmodel, an estimate output of an extent of damage on the one or morecomponents; receiving the estimate output at a damage module;generating, via the dynamic process control model, an operating responseof the installed product to the received estimate output; and inresponse to receiving the estimate output, executing a true-up model togenerate a control action to reduce uncertainty of the estimate output.The inventors note that in the asset or system's current and/or futurestates, it may be appreciated that accurate estimates of damage andperformance, as provided by one or more embodiments, are desired so thatresultant control actions provide the desired physical operations.

A technical effect of some embodiments of the invention is an improvedand/or computerized technique and system for managing system-levelperformance variation by using a system model to identify specifichigh-operating risk assets that may need to be “trued-up” via anintervention/inspection. Embodiments provide for the recommendation ofdifferent levels of intervention depending on the amount of uncertaintyassociated with a damage model output and its ultimate effect on theoperational utility of the asset or system of assets. Embodimentsprovide for a rational, transparent, quantitative approach toquantifying acceptable uncertainty at an individual component level toachieve fleet-wide metrics. Embodiments provide for the identificationof non-obvious, system-level solutions for managing maintenance orperformance risk (e.g., minimizing maintenance cost, maximizingequipment usage), with no compromise on safety. For example, real worldbenefits include accurate current physical and operational stateestimates (e.g., estimated damage and/or performance state of the asset)(such as heat rate, cycle time, tollerancing, etc.) and future stateattainment by controlling the operations of individual assets and theprocesses which use them in the current time for the optimal benefitover one or more of future time intervals. In one or more embodiments,if the model error is not suppressed, the computing control system maynot find an operating control solution or the control actions taken maynot reduce operating risk with respect to the desired states in eitherthe current or future or intervals of time. With this and otheradvantages and features that will become hereinafter apparent, a morecomplete understanding of the nature of the invention can be obtained byreferring to the following detailed description and to the drawingsappended hereto.

Other embodiments are associated with systems and/or computer-readablemedium storing instructions to perform any of the methods describedherein.

DRAWINGS

FIG. 1 illustrates a system according to some embodiments.

FIG. 2 illustrates a flow diagram according to some embodiments.

FIGS. 3A-3B each illustrates a graph according to some embodiments.

FIGS. 4A-4D each illustrates a graph according to some embodiments.

FIGS. 5A-5C each illustrates a graph according to some embodiments.

FIG. 6 illustrates a block diagram of a system according to someembodiments.

FIG. 7 illustrates a block diagram according to some embodiments.

FIG. 8 illustrates several graphs according to some embodiments.

DETAILED DESCRIPTION

Assets, including the asset components, typically acquire damage duringnormal operation. For industries, a challenge is assessing a healthstatus (e.g., state of damage or state of performance) of an asset, andthen scheduling appropriate maintenance or operations to avoidunexpected failures or suboptimal performance. Conventionally, a mix ofsensors and analytics in condition-based and prognostic maintenanceprograms are used to assess the health status. Cumulative damage modelsmay be used to estimate the damage state of components which cannot bedirectly observed in operation. These state estimations are in thepresent and may be anticipatory by virtue of the system's ability tocalculate state in current and future time. These models are used torecommend inspections to: (1) verify the damage and (2) perform a shopvisit repair when uncertainty is detected. Typically, local optimizationis performed at a component level via the condition-based and prognosticmaintenance programs using coarse forecasts (e.g., simple linearextrapolation of the damage state) to determine when a damage state fora component of the asset crosses a certain threshold. This simplethreshold level is typically for a specific component, without anyregard to system-level impacts (e.g., shop scheduling, availability ofparts, etc.) and is limited by a past operations being the same as thefuture paradigm (which the simulation overcomes).

In one or more embodiments, the sensing and control system of the assetis changed to be an integrated physical device with a local and remotecomputing control system with computational elements in one or bothlocal and remote processors. As a consequence, there may be newopportunities to enhance the operational value of some industrial assetsusing novel industrial-focused hardware and software. One or moreembodiments provide an integrated system that controls the physicalstate of the asset and the processes which use it/them through time toprovide the utility for which the system may be purchased or leased.

One or more embodiments provide for the use of system-level goals in oneor more time intervals along with a system model to manage system-levelrisk (global optimization) to identify when and what type ofintervention to perform. While the models are used to predict a damagestate of a component, there may be uncertainty associated with thepredicted or estimated damage state. Embodiments manage system-levelrisk (e.g., uncertainty in one or more desired fleet-level operatinggoals/targets) by using a system model to identify specific high-riskcomponents (e.g., engines with high uncertainty in the damage states),that may need to have the actual damage state “trued-up” via anintervention/inspection to reduce the global system risk to anacceptable level. The global system performance risk may be describedusing the current time interval, however, future damage and performancemay be implemented virtually in the simulator with the current timestate estimation as the (most accurate) starting point.

In one or more embodiments, the simulation subcomponent may executelogic for state estimation in future time just as it may be executed incurrent time, only with changing exogenous conditions and implementingoperations logic. In one or more embodiments, the state estimate incurrent time may be a physical sensing, operational and analyticalcomputation-control point. In one or more embodiments, the resultingstates may be propagated into the future via a computational process. Asused herein, a “true-up” is the process to determine the actual state orat least a better estimate of the actual state of a component in thecurrent time where it benefits a current performance and/or where it maybe used in a state control action to predict and then shape a futuredamage or performance state. In one or more embodiments, a true-up planor control action may include at least one of an inspection method, anin-operation test sequence method and an in-operation data burst method.

As described above, it may be desirable to predict when maintenance fora given component should be scheduled. To make these predictions, modelsmay be used. However, the models may not be one hundred percentaccurate, and there may be uncertainty associated with the model'sprediction. This uncertainty may be so large, that the prediction maynot be helpful. One or more embodiments provide analytics and methods tounderstand when the uncertainty is so large that it may need to be“trued-up,” and methods for “truing-up” the uncertainty. In one or moreembodiments, a “true-up” method or control action may include a testprotocol to execute during operation of the component (e.g.,in-operation test sequence method or in-operation data burst method).One or more embodiments may provide a different method to reduce theuncertainty based on the amount of uncertainty that is needed to bereduced (e.g., trade-off of complex/costly intervention versus an amountof uncertainty reduced). One or more embodiments provide for narrowingthe uncertainty to better predict when a given component should bebrought in for maintenance. One or more embodiments may optimize anetwork of components based on the health status of individualcomponents. For example, in terms of a global optimization, a decisionmay be made that negatively impacts one plane to benefit a fleet ofplanes.

Computational models are used to analyze data and generate results thatmay be used to make assessments and/or predictions of a physical system.An owner or operator of a system might want to monitor a condition ofthe system, or a portion of the system to help make maintenancedecisions, budget predictions, etc.

Some embodiments relate to digital twin modeling. “Digital twin” stateestimation modeling of industrial apparatus and/or other mechanicallyoperational entities may estimate an optimal operating condition,remaining useful life, operating performance such as heat rate or othermetric, of a twinned physical system using sensors, communications,modeling, history and computation. It may provide an answer in a timeframe that is useful, that is, meaningfully priori to a projectedoccurrence of a failure event or suboptimal operation. The informationmay be provided by a “digital twin” of a twinned physical system. Thedigital twin may be a computer model that virtually represents the stateof an installed product. The digital twin may include a code object withparameters and dimensions of its physical twin's parameters anddimensions that provide measured values, and keeps the values of thoseparameters and dimensions current by receiving and updating values viaoutputs from sensors embedded in the physical twin. The digital twin mayhave respective virtual components that correspond to essentially allphysical and operational components of the installed product andcombinations of products or assets that comprise an operation.

As used herein, references to a “digital twin” should be understood torepresent one example of a number of different types of modeling thatmay be performed in accordance with teachings of this disclosure.

The term “installed product” should be understood to include any sort ofmechanically operational entity, including, but not limited to, jetengines, locomotives, gas turbines, and wind farms and their auxiliarysystems as incorporated. The term is most usefully applied to largecomplex systems with many moving parts, numerous sensors and controlsinstalled in the system. The term “installed” includes integration intophysical operations such as the use of engines in an aircraft fleetwhose operations are dynamically controlled, a locomotive in connectionwith railroad operations, or apparatus construction in, or as part of,an operating plant building, machines in a factory or supply chain andetc.

As used herein, the term “automatically” may refer to, for example,actions that may be performed with little or no human interaction.

Turning to FIG. 1, a block diagram of a system 100 architecture isprovided according to some embodiments. The system 100 may include atleast one “installed product” 102. While two installed products 102 areshown herein to represent a fleet of installed products 102, anysuitable number may be used. It is noted that each installed product 102communicates with a platform 107, and elements thereof, in a samemanner, as described below. As noted above, the installed product 102may be, in various embodiments, a complex mechanical entity such as theproduction line of a factory, a gas-fired electrical generating plant, ajet engine on an aircraft amongst a fleet (e.g., two or more aircraftsor other assets), a wind farm, a locomotive, etc. As used herein, theterms “installed product” and “asset” may be used interchangeably. Theinstalled product 102 may include a considerable (or even very large)number of physical elements or components 104, which for example mayinclude turbine blades, fasteners, rotors, bearings, support members,housings, etc. As used herein, the terms “physical element” and“component” may be used interchangeably. The installed product 102 mayalso include subsystems, such as sensing and localized control, in oneor more embodiments.

The system 100 may include a platform 107. In some embodiments, theplatform 107 may include a computer data store 104 that may provideinformation to a damage module 106 and may store results from the damagemodule 106. The damage module 106 may include a true-up model 108, adigital twin 109, a damage metric model 111, and one or more processingelements 110. In one or more embodiments, the damage module 106 mayinclude a performance metric model 113 instead of, or in addition to,the damage metric model 111. In one or more embodiments, the performancemetric model 113 may provide an estimate of system-level operatingperformance on at least one of one or more asset and one or more coupledsystems, in a similar manner to that described below for the damagemetric model 111. In one or more embodiments, the damage module 106 mayinclude a dynamic process control model 115. In one or more embodiments,in response to a received damage estimate, the dynamic process controlmodel 115 may generate a dynamic operating response of the installedproduct 102 (including any appropriate sub-component 103) with respectto its one or more operating parameters. In one or more embodiments, ifthe damage estimate does not result in an operating ramification for theinstalled product, no action may be taken. In one or more embodiments,if the damage estimate does result in an operating ramification for theinstalled product, the system may be perturbed to secure a measurementand/or call for a physical measure, such as an inspection. The processor110 may, for example, be a conventional microprocessor, and may operateto control the overall functioning of the damage module 106. In one ormore embodiments, the processor 110 may be programmed with a continuousor logistical model of industrial processes that use the one or moreinstalled products 102. In one or more embodiments, the processor 110may receive engineered sensing and control data for dynamically updatinga forecasted physical and operational state information from individualassets and the processes which use them with actual system states withrespect to efficiency, life and the process system performance.

In one or more embodiments, the damage module 106 may calculate thecurrent condition of an asset with integrated current time sensor andcontrol inputs, for example, while also simulating the asset and thesystems which use them ahead of real time in order to beneficiallychange the operation of the assets in one time interval to achieve apreferred future performance in another time interval. Reducing theuncertainty of the state estimates provided by the models 108, 109, 111,113, 115 within the system 100 may provide a constantly refreshed statecalculation with the most current asset data, in effect “trueing up” themodels with actual physical and operational states, in one or moreembodiments. In one or more embodiments, the true-up model 108 may bedynamically refreshed.

In one or more embodiments, the damage metric model 111 may estimate anextent of damage on one or more components. In one or more embodiments,the damage metric model 111 may estimate a performance indicator aswell.

In one or more embodiments, the true-up model 108 may allow operators ofthe installed product 102 to generate a plan or recommendation to reduceuncertainty of the damage estimate. In one or more embodiments, thereduced uncertainty state may be used to simulate future operationsforward and dynamically optimize operational set-points and assignmentsbetween current and future time intervals.

In one or more embodiments, the data store 104 may comprise anycombination of one or more of a hard disk drive, RAM (random accessmemory), ROM (read only memory), flash memory, etc. The data store 104may store software that programs the processor 110 and the damage module106 to perform functionality as described herein.

The damage module 106, according to some embodiments, may access thedata store 104 and utilize the true up model 108, the digital twin 109and the damage metric model 111 to create a prediction and/or resultthat may be transmitted to at least one of various user platforms 212,back to the installed product 102 or to other systems (not shown), asappropriate (e.g., for display to a user, operation of the installedproduct, operation of another system, or input to another system).

The damage module 106 may be programmed with one or more softwarecomponents that may model individual physical elements 101 that make upthe installed product 102.

A communication channel 118 may be included in the system 100 to supplydata from at least one of the installed product 102 and the data store104 to the damage module 106.

In some embodiments, the system 100 may also include a communicationchannel 120 to supply output from the true-up model 108, digital twin109 and damage metric model 111 in the damage module 106 to at least oneof user platforms 112, back to the installed product 102, or to othersystems. In some embodiments, signals received by the user platform 112,installed product 102 and other systems may cause modification in thestate or condition or another attribute of one or more physical elements101 of the installed product 102.

Although not separately shown in the drawing, one or more control units,processors, computers or the like may be included in the installedproduct 102 to control operation of the installed product 102, with orwithout input to the control units, etc., from the damage module 106.

As used herein, devices, including those associated with the system 100and any other devices described herein, may exchange information via anycommunication network which may be one or more of a Local Area Network(“LAN”), a Metropolitan Area Network (“MAN”), a Wide Area Network(“WAN”), a proprietary network, a Public Switched Telephone Network(“PSTN”), a Wireless Application Protocol (“WAP”) network, a Bluetoothnetwork, a wireless LAN network, and/or an Internet Protocol (“IP”)network such as the Internet, an intranet, or an extranet. Note that anydevices described herein may communicate via one or more suchcommunication networks.

A user may access the system 100 via one of the user platforms 112(e.g., a control system, personal computer, tablet, or smartphone) toview information about and/or manage the installed product 102 inaccordance with any of the embodiments described herein. According tosome embodiments, an interactive graphical display interface may let anoperator define and/or adjust certain parameters and/or provide orreceive automatically generated recommendations or results.

Turning to FIGS. 2-5, a flow diagram and associated graphs, of anexample of operation according to some embodiments is provided. Inparticular, FIG. 2 provides a flow diagram of a process 200, accordingto some embodiments. Process 200, and any other process describedherein, may be performed using any suitable combination of hardware(e.g., circuit(s)), software or manual means. For example, acomputer-readable storage medium may store thereon instructions thatwhen executed by a machine result in performance according to any of theembodiments described herein. In one or more embodiments, the system 100is conditioned to perform the process 200 such that the system is aspecial-purpose element configured to perform operations not performableby a general-purpose computer or device. Software embodying theseprocesses may be stored by any non-transitory tangible medium includinga fixed disk, a floppy disk, a CD, a DVD, a Flash drive, or a magnetictape. Examples of these processes will be described below with respectto embodiments of the system, but embodiments are not limited thereto.The flow chart(s) described herein do not imply a fixed order to thesteps, and embodiments of the present invention may be practiced in anyorder that is practicable.

Initially at S210, an installed product 102 accumulates damage duringoperation thereof. In a non-exhaustive example used herein, theinstalled product 102 may be an aircraft, and the component 101 may bethe engine. In one or more embodiments, the component 101 may includeone or more sub-components 103. In the non-exhaustive example, asub-component may be a blade in the engine. As described above,embodiments may include other suitable installed products 102,components 101 and sub-components 103.

Then at S212, the damage module 106 may determine via execution of thedamage metric model 111 a damage estimate for at least one of: one ormore components 101 and one or more sub-components 103.

In one or more embodiments, the damage metric model 111 may be used toestimate/calculate an extent of damage on a component. The damage metricmodel 111 may receive as input one or more measured sensor values fromthe installed product 102 or computer data store 104, and in someinstances operating conditions, to output an estimated damage for agiven component. As described above, a single component 101 may includemultiple sub-components 103, and therefore the damage metric model 111may be executed at least once for each sub-component 103. The inventorsnote that even at the sub-component level, there may be multiple damagemodes (e.g., a blade may have damage at a root thereof, or a tipthereof, etc.) Then, the determined damage estimate for eachsub-component 103 may be aggregated to provide a damage estimate for thecomponent 101.

Turning to FIG. 3A, a graph 300 is provided, showing a progression of asingle damage or deterioration mode for a sub-component (e.g.,spallation growth on a blade over time). The graph 300 may show theprobability distribution of a physical parameter (e.g., spallationgrowth) as a damage mechanism over time, which may have an associateduncertainty in an operational decision (e.g., when to take the engine infor maintenance). Inputs to the damage metric model 111 may include, forexample, a particular speed, temperature, and pressure at which theblade is being flown to see how the crack may grow. Since it's a model,there may be uncertainty around the model. As such, when forecasts aremade, there may be uncertainty with the model and uncertainty about whatwill happen in the future and how the blade will fly. The inventors notethat the simulation component of the system may present hypotheticaloperations to the state estimation and compute current and future timeintervals under various operating and maintenance assumptions.

In one or more embodiments, a failure may occur when the damage crossesa threshold 302. In one or more embodiments, the concept of thresholdmay be generalizable to any conditional logic (e.g., not just a simplethreshold). At t_(initial) 304, the damage may have a certain value thatincreases in time as flights get flown, as indicated by the solid thickline 306. In one or more embodiments, the damage 306 indicated by thesolid thick line may be based on measured input of known parameters. Theuncertainty in the model's prediction of the extent of damage 308, maygrow in time due to uncertainty one or more factors, including but notlimited to measurements and sensor readings.

At t_(present) 310, the estimated damage may have a mean value 312 and adistribution of uncertainty 314 around the mean.

In one or more embodiments, the damage module 106 may make a forecast ofthe future damage growth 316 (dotted line), which may result in anestimate of the time at which the threshold 302 will be crossed(t_(threshold) 318). The inventors note that this estimate may be basedon assumptions about how the plane/engine/blade will fly and may beprobabilistic with a distribution and/or provided by the simulator. Forexample, the future flights may be the same as the past flights, and/orthere may be a schedule of flights to determine how theplane/engine/blade will fly. The estimate for t_(threshold) 318 may alsohave a distribution of uncertainty 314, as there may be a distributionof uncertainty of the time when the damage will cross the threshold 302.

At time t_(present) 310, the uncertainty in the value of the damageestimate may have a distribution 308, which may result in acorresponding uncertainty in t_(threshold) 318. This uncertainty may beundesirable, and instead of relying on the damage estimate att_(present), a true-up of the damage state of the component/subcomponentmay be performed. As used herein, the term “true-up” means to determinea better estimate (with lower uncertainty) of the actual damage state(e.g., via an inspection, in-operation test sequence, data-burst orother method).

FIG. 3B, provides a graph 350 of the damage state progression, which issimilar to the graph 300 shown in FIG. 3A. The difference is, in FIG.3B, the damage module 106 has “trued-up” at t_(present) via execution ofthe true-up model 108 resulting in a narrower uncertainty in thet_(threshold) 318. In one or more embodiments, the “true-up” may resetthe output of the damage metric model 111 to the actual value att_(present), as shown in FIG. 3B. The actual value of the damage statemay go up or down from the estimate in FIG. 3A, as indicated by thearrow 352. In the graph 350 provided herein, the crack is bigger thanwhat the model predicted, as indicated by the true state 354. Aftertrueing-up the output of the damage metric model 111, the uncertainty isreduced at t_(present), as there may still be some uncertaintyremaining. As shown in the graph 350, the uncertainty distribution 308may now grow from t_(present) (rather than t_(initial)), resulting in asmaller uncertainty at t_(threshold) 318 because of the trued-up output.

Turning back to the process 200, in S213, an uncertainty associated witheach of the damage estimates is determined.

Then in S214, the damage module 106 may aggregate the determined damageestimates for the component(s) 101 and/or sub-component(s) 103 togenerate an overall health state of the installed product 102 and/orcomponent 101, respectively, and an aggregate uncertainty associatedwith the aggregate damage estimate. In one or more embodiments, thefeatures of S214 may be active for any time period—past, current orfuture, as the uncertainty it is controlling is damage state estimation.As described above, there may be more than one damage mode for acomponent 101 (e.g., one damage mode associated with an estimatedescribing crack growth on a blade, another damage mode associated withan estimate describing pitting of the blade). The damage estimate may becalculated for each damage mode, as described above, and any one of thedamage estimates crossing the threshold may result in an engine failure,for example.

One or more embodiments provide for processing control, which may enablethe optimal selection and weighting of sundry damage analytics throughone or more time periods (e.g., a current time and a simulated futuretime interval). For example, FIGS. 4A, 4B and 4C each show a graph 430,425 and 420, respectively, of a damage estimate for a particular damagemode (e.g., excessive clearances between rotating and fixed components,wear in coating/erosion on the blade, a crack on a blade tip or othercomponent in the system) at a point or interval of time. Each of thesedamage estimates (and their associated uncertainty probabilities) may beaggregated to give an overall view of the probability of failure for theentire engine, as shown in FIG. 4D. The graph 400 in FIG. 4D may providean aggregated view of estimated damage (mean and distribution) oft_(threshold) 418 for the entire engine. For reasons such as analyticalprecision due to certain methods for estimation in a given failure orperformance process, a given model 420, 425, 430 may have comparativestrength singularly, or in combination with certain other estimatoranalytics, as described further below. One or more embodiments provide acomputing control mechanism which may optimally select the one or moreanalytical estimators.

In one or more embodiments, at present time 435, a desired specificity445 may be needed such as to differentiate between two repair oroperating modes for a certain control action in the physical world, suchas the acquisition of state measurement information. The estimatedspecificity may not differentiate a control action 450 and the system100 may seek to boost the precision for such differentiation via twosteps 455. A first step may be the selection of a candidate damageanalytic (e.g., digital twin) 460 as a binary decision where one or morecandidate analytics may be selected. A second step may weight thecontribution of the selected analytic 465. In a similar way, at anestimated threshold value 418, an actual or forecast value 440 may falloutside of the differentiation of one physical system control actionversus another 445, and the error may be iterated down 450 untilcombinations of damage analytics may be explored 460, 465 in either aparametric search space or a goal seeking optimization method, forexample. Other suitable exploration methods may be used. In eitheranalytical selection or weighting, embodiments may minimize thevariation of damage estimate sufficient to differentiate the controlaction in the physical world which selects the true-up method and timing470. An example true-up action may be the use of one or more other assetstate estimates in a portfolio of assets or the assignment of a physicalstate estimation data acquisition on one or more assets at one or moretimes, for example. Other suitable true-up actions may be used.

For one or more periods of time (t₁→t_(n)) 470, the estimated damage maybe compared to a desired specificity 445 sufficient to differentiatecontrol actions, in one or more embodiments. The as-is or simulatedfuture damage or performance estimated may be compared to the requiredor desired specificity and the error may be calculated, in one or moreembodiments. Then the one or more candidate(s) damage analytic orperformance analytic may be selected 460 for inclusion into theconsensus estimate 400 until the variation is minimized 435, 440 byinclusion alone. For the included analytics, their contributing weights465 may then be iterated upon until the desired specificity 445 is met.In one or more embodiments, the search for inclusion and weighting ofthe damage analytic(s) may be a full enumeration method, which may becomputed one or more computing nodes, an evolutionary method (e.g.,Heuristic or Gradient Search), or any other suitable method. Forselected analytics, their contributing weights may be selected byoptimization methods (e.g., full enumeration, search based, or any othersuitable optimization method). The desired specificity 445 maydifferentiate between maintenance and/or control actions and does notviolate engineering or operating limits over one or multiple intervalsof time.

In one or more embodiments, in the selection for inclusion 460 orweighting 465, features related to asset-use may be used todifferentially use or weight the damage or performance analytic. Someexamples of these features may include cycles, time, rate of damageestimation change as a function of operations or time or triggeringstate changes from other assets under management that act as leadingindicators or proxies for a given set of assets or systems whose damageor performance is being computed for with the system 100. Other suitableexamples of features may be used.

Turning back to process 200, in parallel to S214, an operating responseto the received damage estimate in S213 is generated in S215. In one ormore embodiments, there may be uncertainty related to simulated forwardsystem response dynamics. In one or more embodiments, the features ofS215 are directed to system performance that may be calculated with adynamical system transfer function (e.g., a discrete event simulation).The system performance may be a consequence of damage.

Then in S216, it is determined if the uncertainty associated with theaggregate damage estimate at t_(threshold) 418 is too high. In one ormore embodiments, there are at least two means, that are not mutuallyexclusive, that may make the uncertainty too high: the featuresdescribed in S214 and the features described in S215. In one or moreembodiments, the determination that the aggregate damage estimate att_(threshold) 418 is too high may be highly dependent on operations. Inone or more embodiments, the damage module 106 and true-up model 108 mayreceive as an input one or more system-level metrics, which may be usedto determine if the uncertainty associated with the aggregate estimateat t_(threshold) 418 is too high. In one or more embodiments, thesystem-level metrics may provide an understanding of broader systemoperations (e.g., for the entire fleet of planes) to determine whichplane(s) needs to have damage estimates trued-up to achieve the broaderfleet-level metric (e.g., percentage of flights flown, yield percentage,a financial metric, on-time departures).

In one or more embodiments, the broader fleet-level metric may be ameasure of something that may not be a direct engineering damagequantification. In one or more embodiments, the fleet-level metric maybe a financial goal, on-time departure goal, or other operationalmetric, that does not compromise safety. For example, an airline mayreally care about flying passengers, and may place a greater emphasis ondelivering ninety percent of their passengers on time (fleet levelmetric) than on a deterioration in a blade (e.g., spalling, excessiveclearances, cracking, fatiguing and etc.) that lowers its efficiency(engineering damage quantification) and does not compromise safety.

In one or more embodiments, the damage module 106 may use a “system ofsystem” simulation (e.g., a digital twin operations optimization (DTOO)simulation) 109 to understand (e.g., quantify) a mean and distributionof the fleet-level metric if no operational changes are made to theexisting component, and to compare this fleet-level metric against atarget in and through time. For example, FIG. 5A shows a mean anddistribution for a fleet-level metric “as-is,” with no change 502, 504,respectively, and for a desired target 506, 508, respectively. In one ormore embodiments, the DTOO simulation 109 may be used with the true-upmodel 108 to aggregate the damage estimates for the sub-components, tothe components, to the engines, to the planes to the fleet of planes, todetermine if the fleet-level metric is met. In one or more embodiments,if the target is not met, the DTOO simulation 109 may be used todetermine which component(s) need to be trued-up in order to meet theoverall fleet target through time. For example, the DTOO simulation 109may indicate which components need to be trued-up. The inventors notethat determining which component needs to be trued-up to meet theoverall fleet target may not be obvious, as some components (e.g.,engines) may be allowed to have larger uncertainties (e.g., depending onthe routes they will fly) and other components may be required to havelower uncertainties (if, for example, flying a more severe route). Inone or more embodiments, there may and may not be a same acceptableuncertainty for all of the components in the fleet, and the uncertaintymay be highly dependent on expected operations for the component.

It may be desirable to reduce the overall t_(threshold) 418 uncertaintyto improve scheduling of preventative maintenance and avoid componentfailure, (e.g., to maximize engine use, while not compromising safety.It may be desirable to accurately schedule maintenance because if actingconservatively, a part may be removed from action early, losing life onthe part, and if the part is removed from action too late, there may bepart degradation that impacts performance or failure. The inventors notethat improving scheduling of preventative maintenance may have rippleeffects on, for example, shop loading (e.g., there are only a finitenumber of shops that can perform the maintenance, and there is a need tomake sure there are time slots available to perform the maintenance),and parts and inventory management (e.g., parts may be expensive and ashop may not want to carry them if they do not need them). The inventorsfurther note, for example, that FIG. 4D is for a single engine, but eachplane has at least two engines, and a fleet of two or more planes mayhave hundreds of engines, so the scheduling/preventative maintenancegoals may increase exponentially.

Turning back to process 200, in S216 it is determined that theuncertainty is not too high, the process 200 ends at S218.

If in S216, it is determined that the t_(threshold) 418 uncertainty istoo high (e.g., the uncertainty is so large, t_(threshold) 418 may notprovide meaningful information for scheduling maintenance), the process200 proceeds to S220, and it is determined which damage estimates thatform the aggregate need to be trued-up (e.g, back up from the componentlevel). One or more embodiments, may determine which damage estimate iscausing the largest amount of uncertainty in the aggregate. For example,most of the uncertainty associated with the component in FIG. 4D is fromthe damage mode associated with FIG. 4A, while the damage modesassociated with FIGS. 4B and 4C provide little uncertainty.

After it is determined which damage estimate is causing the largestamount of uncertainty in the aggregate, the amount of true-up needed toreduce the uncertainty for the damage estimate to an acceptable levelmay be quantified in S222. In one embodiment, the system simulator maybe used to calculate the contribution to variance of the damage orperformance at one or more intervals of time by calculating replicationsand the partial derivatives of state estimation on the overall systemperformance or damage metrics.

While a true-up by inspection may result in very little to nouncertainty regarding the damage estimate of the component, theinventors note that inspections may be costly and that from a globaloptimization perspective, it may be desirable to perform another form oftrue-up that decreases the uncertainty by some amount less than almost100% (e.g., perform a true-up that makes the uncertainty 20% better).For example, a fleet includes five planes, one of which has about a 6month window in which it will need maintenance. The 6 month window maybe too great, and a DTOO simulation model may be run to get the windowdown to 2 months, which is an acceptable metric, or maybe down to aspecific day because the shops are very constrained. In one or moreembodiments, one or more constraints may affect an acceptable level ofuncertainty. In one or more embodiments, some constraints may be imposedby the model itself (e.g., indeterminate repair work-scope or assignmentof damage or performance at the system level with enough specificity toa sub component), while other constraints may come from operationalsituations (e.g., limiting the maximum thrust or rate of work output orpatterns of usage of the asset or an on-wing maintenance event such as acertain modality of washing or location of washing).

Then in S224 one or more improved uncertainties is estimated. In one ormore embodiments, the damage module 106 may, via the true-up model 108,apply one or more true-up methods to the DTOO simulation 109 to acquiremore information to provide an estimate of improved uncertaintyassociated with application of each of the true-up methods.

A first true-up method may be an inspection. While an inspection mayprovide the best uncertainty reduction, it may involve taking thecomponent out of service, and therefore may be the most intrusive andexpensive.

A second true-up method may be an “in-operation test sequence.” In oneor more embodiments, the “in-operation test sequence,” may include aspecified sequence of operational control inputs designed specificallyto infer a measure of damage (e.g., controlling the operation of thecomponent differently from usual operations, but within operatingparameters, to obtain more data). The inference maybe direct (e.g.,directly provides the data) or may be data that is fed to another DTOO109 to provide a better idea of the damage or health of the system. Forexample, the “in-operation test sequence,” may include a pre-definedengine operating test sequence of a particular run-up just prior totake-off, or a particular engine cycling at cruise level. The inventorsnote that the “in-operation test sequence” may improve the uncertaintyless than the inspection plan. The “in-operation test sequence” may beexecuted while the component is in-operation by line personnel (e.g.,pilot or maintenance staff), so there may be no need to remove thecomponent out of service, which may make the “in-operation testsequence” plan less expensive and less obtrusive than the inspectionplan. In one or more embodiments, the “in-operation test sequence” maybe a manual test protocol (e.g., a pilot follows a checklist) or may bea fully automated “press a button” (e.g., a signal is sent to a planeand the plane runs the sequence without pilot involvement). The cost anddisruption to perform an in operations test sequence being minor, it maybe beneficially implemented before a more invasive true-up method iscalled. The optimal selection of sequencing may be calculated by thecomputing system.

A third true-up method may be an “in-operation data burst” enabled withthe integrated sensing, control, data and computing system. In one ormore embodiments, the “in-operation data burst” may be a pre-defined“data-burst” sensor and control recording plus associated analyticalprocessing that may provide a better estimate of the damage. In one ormore embodiments, the “in-operation data burst,” may be a more focuseddata collection (e.g., recording data at a higher-frequency, recordingadditional parameters not usually saved or edge processing). In one ormore embodiments, the “in-operation data burst” may be passive, and maynot affect the component at all, as this plan focuses on data gathering.A benefit of one or more embodiments, is that the “in-operation databurst” may have zero operational impact, and may be a purely datagathering and analysis exercise, which may make it less expensive thanboth the inspection plan and the “in-operation test sequence” plan. Inone or more embodiments, the “in-operation data burst” may be fullyautomated, and the data may be transmitted via wireless/satellite/ormanually extracted at an appropriate time. The “in-operation data burst”plan may improve the uncertainty less than the inspection plan.

Then in S226, the damage module 106 may generate a recommendation of acontrol action (i.e., inspection, in-operation test sequence, orin-operation data-burst) to true-up the uncertainty of the damageestimate for the component based on the method that improves theestimated in the most desirable manner given a set of parameters. In oneor more embodiments, the control action may be an automated perturbationof the installed product or a specified physical measurement. FIG. 5Bshows a mean and distribution of a fleet-wide metric for a forecast thattakes the recommended true-ups into account 510, 512, respectively, ascompared to the mean and distribution of the fleet-wide metric target506, 508, respectively. In one or more embodiments, the recommendationof a particular method or plan may impact the mean, the variance orboth. For example, in a non-exhaustive scenario, the estimate for adeterioration of a certain physical clearance might be extremely large,but also with very large uncertainty, essentially recommending that anengine be immediately removed for inspection. An example of physicalclearance deterioration is between compression or expansion stages of aturbine, where pressure drop loss occurs between rotating and fixedcomponents of the apparatus and as a consequence, energy that could havebeen transmitted to the rotating shaft of the turbine is lost throughbypassed routes enabled by the clearances, thus lowering thethermodynamic performance of the system. Because of the largeuncertainty, it may be desirable to perform some form of true-up. Ifthere are no other components requiring maintenance in the near future,it may be desirable to perform an inspection to get the best possible(lowest) uncertainty at this time in order to maximize the on-wingengine time. If, however, the engine is likely going to require a shopvisit in the near future due to other components, than perhaps a loweruncertainty (and less burdensome) true-up method (such as thedata-burst) may be sufficient to provide confidence the component willsurvive until the next maintenance action.

In S228, the recommended control action is executed. As used herein, theterms “true-up plan” and “control action” may be used interchangeably.In one or more embodiments, after the recommended true-up(s) areexecuted, a post-assessment may be performed to determine how effectivethe true-up method was at decreasing the uncertainty. For example, FIG.5C shows the distribution of the fleet-wide metric after execution ofthe true-up 514. In this example, the post-true-up distribution 514 didnot meet the target distribution 512, but is better than the as-isdistribution 504. If the post-true-up distribution 514 does not meet thetarget distribution 512, an in-situ inspection of the component may bescheduled, in one or more embodiments. It may be determined in one ormore embodiments, that an in-situ inspection may not need to bescheduled even if the post-true-up distribution 514 does not meet thetarget distribution 512, as the post-true-up distribution 514 may beclose enough to the target distribution 512 for a given metric.

Note the embodiments described herein may be implemented using anynumber of different hardware configurations. For example, FIG. 6illustrates a health assessment platform 600 that may be, for example,associated with the system 100 of FIG. 1. The health assessment platform600 comprises a health assessment processor 610 (“processor”), such asone or more commercially available Central Processing Units (CPUs) inthe form of one-chip microprocessors, coupled to a communication device620 configured to communicate via a communication network (not shown inFIG. 6). The communication device 620 may be used to communicate, forexample, with one or more users. The health assessment platform 600further includes an input device 640 (e.g., a mouse and/or keyboard toenter information) and an output device 650 (e.g., to output and displaythe assessment and recommendation).

The processor 610 also communicates with a memory/storage device 630.The storage device 630 may comprise any appropriate information storagedevice, including combinations of magnetic storage devices (e.g., a harddisk drive), optical storage devices, mobile telephones, and/orsemiconductor memory devices. The storage device 630 may store a program612 and/or health assessment processing logic 614 for controlling theprocessor 610. The processor 610 performs instructions of the programs612, 614, and thereby operates in accordance with any of the embodimentsdescribed herein. For example, the processor 610 may receive data andthen may apply the instructions of the programs 612, 614 to determine amethod for improving an uncertainty estimation.

The programs 612, 614 may be stored in a compressed, uncompiled and/orencrypted format. The programs 612, 614 may furthermore include otherprogram elements, such as an operating system, a database managementsystem, and/or device drivers used by the processor 610 to interfacewith peripheral devices.

As used herein, information may be “received” by or “transmitted” to,for example: (i) the platform 600 from another device; or (ii) asoftware application or module within the platform 600 from anothersoftware application, module, or any other source.

It is noted that while progress with industrial equipment automation hasbeen made over the last several decades, and assets have become‘smarter,’ the intelligence of any individual asset pales in comparisonto intelligence that can be gained when multiple smart devices areconnected together. Aggregating data collected from or about multipleassets may enable users to improve business processes, for example byimproving effectiveness of asset maintenance or improving operationalperformance, if appropriate. Industrial-specific data collection andmodeling technology may be developed and applied.

In an example, an industrial asset may be outfitted with one or moresensors configured to monitor respective ones of an asset's operationsor conditions. Data from the one or more sensors may be recorded ortransmitted to a cloud-based or other remote computing environment. Bybringing such data into a cloud-based computing environment, newsoftware applications informed by industrial process, tools and know-howmay be constructed, and new physics-based analytics specific to anindustrial environment may be created. Insights gained through analysisof such data may lead to enhanced asset designs, or to enhanced softwarealgorithms for operating the same or similar asset at its edge, that is,at the extremes of its expected or available operating conditions.

The systems and methods for managing industrial assets may include ormay be a portion of an Industrial Internet of Things (IIoT). In anexample, an IIoT connects industrial assets, such as turbines, jetengines, and locomotives, to the Internet or cloud, or to each other insome meaningful way. The systems and methods described herein mayinclude using a “cloud” or remote or distributed computing resource orservice. The cloud may be used to receive, relay, transmit, store,analyze, or otherwise process information for or about one or moreindustrial assets. In an example, a cloud computing system may includeat least one processor circuit, at least one database, and a pluralityof users or assets that may be in data communication with the cloudcomputing system. The cloud computing system may further include, or maybe coupled with, one or more other processor circuits or modulesconfigured to perform a specific task, such as to perform tasks relatedto asset maintenance, analytics, data storage, security, or some otherfunction.

However, the integration of industrial assets with the remote computingresources to enable the IIoT often presents technical challengesseparate and distinct from the specific industry and from computernetworks, generally. A given industrial asset may need to be configuredwith novel interfaces and communication protocols to send and receivedata to and from distributed computing resources. Given industrialassets may have strict requirements for cost, weight, security,performance, signal interference, and the like, such that enabling suchan interface is rarely as simple as combining the industrial asset witha general purpose computing device.

To address these problems and other problems resulting from theintersection of certain industrial fields and the IIoT, embodiments mayenable improved interfaces, techniques, protocols, and algorithms forfacilitating communication with, and configuration of, industrial assetsvia remote computing platforms and frameworks. Improvements in thisregard may relate to both improvements that address particularchallenges related to particular industrial assets (e.g., improvedaircraft engines, wind turbines, locomotives, medical imaging equipment)that address particular problems related to use of these industrialassets with these remote computing platforms and frameworks, and alsoimprovements that address challenges related to operation of theplatform itself to provide improved mechanisms for configuration,analytics, and remote management of industrial assets.

The Predix™ platform available from GE is a novel embodiment of suchAsset Performance Management Platform (APM) technology enabled by stateof the art cutting edge tools and cloud computing techniques that mayenable incorporation of a manufacturer's asset knowledge with a set ofdevelopment tools and best practices that may enable asset users tobridge gaps between software and operations to enhance capabilities,foster innovation, and ultimately provide economic value. Through theuse of such a system, a manufacturer of industrial assets can beuniquely situated to leverage its understanding of industrial assetsthemselves, models of such assets, and industrial operations orapplications of such assets, to create new value for industrialcustomers through asset insights.

The further advancement in sensing, control, simulation and optimizationin systems such as the disclosed extends past the current art APMlimitations of past operations=future operations and little ability totrade off actions in maintenance or operations in the current period forthose of future periods where optimal interventions may be made such asmaintenance, assignment, duty limitations, through time, having thebenefit of far more accurate starting conditions afforded by thetrue-up.

FIG. 7 illustrates generally an example of portions of a first APM 700.As further described herein, one or more portions of an APM may residein an asset cloud computing system 720, in a local or sandboxedenvironment, or may be distributed across multiple locations or devices.An APM may be configured to perform any one or more of data acquisition,data analysis, or data exchange with local or remote assets, or withother task-specific processing devices.

The first APM 700 may include a first asset community 702 that may becommunicatively coupled with the asset cloud computing system 720. Aplurality of assets 702, 703 may be subject to state estimation at oneor more multiple time intervals. Within a given industrial system, theremay be estimations of state for more than one system r sub-system suchas a blade of a wind turbine 701 or gear 704 associated with the turbine701. These sub-systems may be combined to estimate the state of damageand performance for an integrated system such as, for example, awindmill, an aircraft engine, a power plant and a train. Other suitableintegrated systems may be used. In an example, a machine module 710receives information from, or senses information about, at least oneasset member of the first asset community 702, and configures thereceived information for exchange with the asset cloud computing system720. In an example, the machine module 710 is coupled to the asset cloudcomputing system 720 or to an enterprise computing system 730 via acommunication gateway 705.

In an example, the communication gateway 705 includes or uses a wired orwireless communication channel that may extend at least from the machinemodule 710 to the asset cloud computing system 720. The asset cloudcomputing system 720 includes several layers. In an example, the assetcloud computing system 720 includes at least a data infrastructurelayer, a cloud foundry layer, and modules for providing variousfunctions. In the example of FIG. 7, the asset cloud computing system720 includes an asset module 721, an analytics module 722, a dataacquisition module 723, a data security module 724, and an operationsmodule 725. Each of the modules 721-725 includes or uses a dedicatedcircuit, or instructions for operating a general purpose processorcircuit, to perform the respective functions. In an example, the modules721-725 are communicatively coupled in the asset cloud computing system720 such that information from one module may be shared with another. Inan example, the modules 721-725 are co-located at a designateddatacenter or other facility, or the modules 721-725 can be distributedacross multiple different locations.

An interface device 740 may be configured for data communication withone or more of the machine module 710, the gateway 705, or the assetcloud computing system 720. The interface device 740 may be used tomonitor or control one or more assets. In an example, information aboutthe first asset community 702 is presented to an operator at theinterface device 740. The information about the first asset community702 may include information from the machine module 710, or theinformation may include information from the asset cloud computingsystem 720. In an example, the information from the asset cloudcomputing system 720 may include information about the first assetcommunity 702 in the context of multiple other similar or dissimilarassets, and the interface device 740 may include options for optimizingone or more members of the first asset community 702 based on analyticsperformed at the asset cloud computing system 720.

In an example, an operator selects a parameter update for the first windturbine 701 using the interface device 740, and the parameter update ispushed to the first wind turbine via one or more of the asset cloudcomputing system 720, the gateway 705, and the machine module 710. In anexample, the interface device 740 is in data communication with theenterprise computing system 730 and the interface device 740 provides anoperation with enterprise-wide data about the first asset community 702in the context of other business or process data. For example, choiceswith respect to asset optimization may be presented to an operator inthe context of available or forecasted raw material supplies or fuelcosts. In an example, choices with respect to asset optimization may bepresented to an operator in the context of a process flow to identifyhow efficiency gains or losses at one asset may impact other assets. Inan example, one or more choices described herein as being presented to auser or operator may alternatively be made automatically by a processorcircuit according to earlier-specified or programmed operationalparameters. In an example, the processor circuit may be located at oneor more of the interface device 740, the asset cloud computing system720, the enterprise computing system 730, or elsewhere.

Returning again to the example of FIG. 7 some capabilities of the firstAPM 700 are illustrated. The example of FIG. 7 includes the first assetcommunity 702 with multiple wind turbine assets, including the firstwind turbine 701. Wind turbines are used in some examples herein asnon-limiting examples of a type of industrial asset that can be a partof, or in data communication with, the first AMP 700.

In an example, the multiple turbine members of the asset community 702include assets from different manufacturers or vintages. The multipleturbine members of the asset community 702 may belong to one or moredifferent asset communities, and the asset communities may be locatedlocally or remotely from one another. For example, the members of theasset community 702 may be co-located on a single wind farm, or themembers may be geographically distributed across multiple differentfarms. In an example, the multiple turbine members of the assetcommunity 702 may be in use (or non-use) under similar or dissimilarenvironmental conditions, or may have one or more other common ordistinguishing characteristics.

FIG. 7 further includes the device gateway 705 configured to couple thefirst asset community 702 to the asset cloud computing system 720. Thedevice gateway 705 may further couple the asset cloud computing system720 to one or more other assets or asset communities, to the enterprisecomputing system 730, or to one or more other devices. The first AMP 700thus represents a scalable industrial solution that extends from aphysical or virtual asset (e.g., the first wind turbine 701) to a remoteasset cloud computing system 720. The asset cloud computing system 720optionally includes a local, system, enterprise, or global computinginfrastructure that can be optimized for industrial data workloads,secure data communication, and compliance with regulatory requirements.

In an example, information from an asset, about the asset, or sensed byan asset itself is communicated from the asset to the data acquisitionmodule 724 in the asset cloud computing system 720. In an example, anexternal sensor may be used to sense information about a function of anasset, or to sense information about an environment condition at or nearan asset. The external sensor may be configured for data communicationwith the device gateway 705 and the data acquisition module 724, and theasset cloud computing system 720 may be configured to use the sensorinformation in its analysis of one or more assets, such as using theanalytics module 722.

In an example, the first AMP 700 may use the asset cloud computingsystem 720 to retrieve an operational model for the first wind turbine701, such as using the asset module 721. The model may be stored locallyin the asset cloud computing system 720, or the model may be stored atthe enterprise computing system 730, or the model may be storedelsewhere. The asset cloud computing system 720 may use the analyticsmodule 722 to apply information received about the first wind turbine701 or its operating conditions (e.g., received via the device gateway705) to or with the retrieved operational model. Using a result from theanalytics module 722, the operational model may optionally be updated,such as for subsequent use in optimizing the first wind turbine 701 orone or more other assets, such as one or more assets in the same ordifferent asset community. For example, information about the first windturbine 701 may be analyzed at the asset cloud computing system 720 toinform selection of an operating parameter for a remotely located secondwind turbine that belongs to a different second asset community.

The first AMP 700 includes a machine module 710. The machine module 710may include a software layer configured for communication with one ormore industrial assets and the asset cloud computing system 720. In anexample, the machine module 710 may be configured to run an applicationlocally at an asset, such as at the first wind turbine 701. The machinemodule 710 may be configured for use with, or installed on, gateways,industrial controllers, sensors, and other components. In an example,the machine module 710 includes a hardware circuit with a processor thatis configured to execute software instructions to receive informationabout an asset, optionally process or apply the received information,and then selectively transmit the same or different information to theasset cloud computing system 720.

In an example, the asset cloud computing system 720 may include theoperations module 725. The operations module 725 may include servicesthat developers may use to build or test Industrial Internetapplications, or the operations module 725 may include services toimplement Industrial Internet applications, such as in coordination withone or more other AMP modules. In an example, the operations module 725includes a microservices marketplace where developers may publish theirservices and/or retrieve services from third parties. The operationsmodule 725 can include a development framework for communicating withvarious available services or modules. The development framework mayoffer developers a consistent look and feel and a contextual userexperience in web or mobile applications.

In an example, an AMP may further include a connectivity module. Theconnectivity module may optionally be used where a direct connection tothe cloud is unavailable. For example, a connectivity module may be usedto enable data communication between one or more assets and the cloudusing a virtual network of wired (e.g., fixed-line electrical, optical,or other) or wireless (e.g., cellular, satellite, or other)communication channels. In an example, a connectivity module forms atleast a portion of the gateway 705 between the machine module 710 andthe asset cloud computing system 720.

In an example, an AMP may be configured to aid in optimizing operationsor preparing or executing predictive maintenance for industrial assets.An AMP may leverage multiple platform components to predict problemconditions and conduct preventative maintenance, thereby reducingunplanned downtimes in the near term or through time by intentionalintervention. In an example, the machine module 710 is configured toreceive or monitor data collected from one or more asset sensors and,using physics-based analytics (e.g., finite element analysis or someother technique selected in accordance with the asset being analyzed),detect error conditions based on a model of the corresponding asset. Inan example, a processor circuit applies analytics or algorithms at themachine module 710 or at the asset cloud computing system 720, oneembodiment having the analytic being a discrete event simulator whichchanges the exogenous conditions (e.g. weather) and control points,maintenance events, work-scopes and locations, runs replications andcomputes confidence intervals and contribution to variance over one ormore time intervals. In another embodiment, the analytic being a dynamicprogramming modality. In another embodiment, the analytic being aMonte-Carlo modality.

In response to the detected error conditions, the AMP may issue variousmitigating commands to the asset, such as via the machine module 710,for manual or automatic implementation at the asset. In an example, theAMP may provide a shut-down command to the asset in response to adetected error condition. Shutting down an asset before an errorcondition becomes fatal may help to mitigate potential losses or toreduce damage to the asset or its surroundings. In addition to such anedge-level application, the machine module 710 may communicate assetinformation to the asset cloud computing system 720.

In an example, the asset cloud computing system 720 may store orretrieve operational data for multiple similar assets. Over time, datascientists or machine learning may identify patterns and, based on thepatterns, may create improved physics-based analytical models foridentifying or mitigating issues at a particular asset or asset type.The improved analytics may be pushed back to all or a subset of theassets, such as via multiple respective machine modules 710, toeffectively and efficiently improve performance of designated (e.g.,similarly-situated) assets.

In an example, the asset cloud computing system 720 includes aSoftware-Defined Infrastructure (SDI) that serves as an abstractionlayer above any specified hardware, such as to enable a data center toevolve over time with minimal disruption to overlying applications. TheSDI enables a shared infrastructure with policy-based provisioning tofacilitate dynamic automation, and enables SLA mappings to underlyinginfrastructure. This configuration may be useful when an applicationrequires an underlying hardware configuration. The provisioningmanagement and pooling of resources may be done at a granular level,thus allowing optimal resource allocation.

In a further example, the asset cloud computing system 720 is based onCloud Foundry (CF), an open source PaaS that supports multiple developerframeworks and an ecosystem of application services. Cloud Foundry canmake it faster and easier for application developers to build, test,deploy, and scale applications. Developers thus gain access to thevibrant CF ecosystem and an ever-growing library of CF services.Additionally, because it is open source, CF can be customized for IIoTworkloads.

The asset cloud computing system 720 may include a data services modulethat may facilitate application development. For example, the dataservices module may enable developers to bring data into the asset cloudcomputing system 720 and to make such data available for variousapplications, such as applications that execute at the cloud, at amachine module, or at an asset or other location. In an example, thedata services module may be configured to cleanse, merge, or map databefore ultimately storing it in an appropriate data store, for example,at the asset cloud computing system 720. A special emphasis has beenplaced on time series data, as it is the data format that most sensorsuse.

Security may be a concern for data services that deal in data exchangebetween the asset cloud computing system 720 and one or more assets orother components. Some options for securing data transmissions includeusing Virtual Private Networks (VPN) or an SSL/TLS model. In an example,the first AMP 700 may support two-way TLS, such as between a machinemodule and the security module 724. In an example, two-way TLS may notbe supported, and the security module 724 may treat client devices asOAuth users. For example, the security module 724 may allow enrollmentof an asset (or other device) as an OAuth client and transparently useOAuth access tokens to send data to protected endpoints.

A plurality of assets 702, 703 may be subject to state estimation at oneor multiple time intervals. Within a given industrial system, there maybe estimations of state for more than one system or sub-system such asthe blade 701 or the gear 704. These subsystems may be combined toestimate the state of damage and performance for an integrated systemsuch as a windmill or aircraft engine or power plant or train.

Turning to FIG. 8, a creation of a control action 800 for a future timeis provided according to one or more embodiments. In one or moreembodiments, the control action 800 may be based on historical pasttrue-ups (e.g., rate of change of past true-ups), and may optimize asequence of true-ups. The control action 800, in one or moreembodiments, may schedule a place-holder for a physical inspection,which may be canceled at a later date, if proved unnecessary. Theinventors note that scheduling the place-holder may be beneficial inthat operational use of the asset(s) may be such that an unscheduledphysical inspection may be too costly and/or infeasible.

The inventors note that the estimation of state and the specificity ofstate may change through time as a result of improved model precisionand/or use of the assets which typically degrade life or performancewith use. With both improved model precision and/or use of the assets,there may be a progression of accuracy estimation (e.g., 801-810) whichmay proceed through time and observations. As shown in the graph 801,initially there may be a damage or performance estimate which may beequally probable through a metric of use (e.g., time, cycles, load,etc.). This estimate may be for a single asset or sub-system at a singlepoint of time. In one or more embodiments, a current damage and/orperformance state at the time of sensing may be received, and thiscurrent-time high-accuracy state may be set as an initial condition in asimulation (e.g., the digital twin 109).

In one or more embodiments, operational set-points of the digital twin109 may be changed, producing simulated future damage and performancestate estimations for one or more operating and/or design modificationscenarios. The scenarios may be replicated, in one or more embodiments,and the confidence intervals of future simulated system states may beestimated. In response to the digital twin's 109 future state estimates,the precise control interventions into the current time period may beenabled that may differentially consume life and performance of theasset in the current time period for the optimal future realization ofthe digital twin's 109 operation, subject to the model error orconfidence intervals of the simulated future.

For example, an exemplary estimate improvement may be made, as shown ingraph 803, by the inclusion of two assets whose behavior may enable a“best case,” “most likely,” and “worst case” estimate, for example. Inone or more embodiments, the inclusion of a plurality of observableassets and systems over time, as shown in the graph 805, may enable atruer estimate of state, compared to observation of a single asset at asingle point in time. The use of many assets through much time andoperation conditions may improve state estimation to a limit ofspecificity, as shown in graph 807, for example, according to one ormore embodiments. In one or more embodiments, these estimates may beintegrated to form cumulative probabilities, as shown in graph 810, ofthe damage or performance metric of interest, as well as a rate ofchange 811 of state estimation change with respect to a change inprobability or metric value as a function of time or other causal factor(e.g., usage, load, maintenance, environmental conditions, etc.). In oneor more embodiments, the rate of change 811 of at least one of precisionand degradation may be used in forecasting when a true-up may be ideallycalled for and scheduled in the future. In one or more embodiments, thetrue-up event may be scheduled into an operating profile of the one ormore assets of the system 700. In one non-exhaustive example, a certaininspection on a certain component of an aircraft engine may be scheduledin a way that the aircraft may be made available for the inspection andcrews may be assigned to that event. As another non-exhaustive example,the engine may be cycled in a way at a certain point in time such thatthe operations are minimally impacted or the conditions to recover theasset's operations are robust.

As part of the control action 800, a schedule 830 may be generated withoperating durations 832 and 842 set as a function of the rate of changeof state change 811. As a non-exhaustive example, duration 832 may be anestimated threshold trigger point for an inspection. An inspection ofmeasurement event is scheduled 833 for the establishment of a currentestimate 834 at a point in future time. In a first scenario 831, therate of change of measurement specificity 811 is calculated, and thedesired specificity 445 for one action versus another falls below thethreshold at duration 832. As added precision (e.g., 801, 803, 805 and807) is caused by observing more assets, operations and time intervalsand/or that results from the fusion of state estimation analytics 455(FIG. 4), the operating period before inspection may be changed 842,compared to the originally estimated threshold trigger point 832, mayresult in a change in schedule control action or state data acquisition.The first scheduled action 833 may be dynamically rescheduled or droppedaccordingly.

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

It should be noted that any of the methods described herein can includean additional step of providing a system comprising distinct softwaremodules embodied on a computer readable storage medium; the modules caninclude, for example, any or all of the elements depicted in the blockdiagrams and/or described herein. The method steps can then be carriedout using the distinct software modules and/or sub-modules of thesystem, as described above, executing on one or more hardware processors610 (FIG. 6). Further, a computer program product can include acomputer-readable storage medium with code adapted to be implemented tocarry out one or more method steps described herein, including theprovision of the system with the distinct software modules.

This written description uses examples to disclose the invention,including the preferred embodiments, and also to enable any personskilled in the art to practice the invention, including making and usingany devices or systems and performing any incorporated methods. Thepatentable scope of the invention is defined by the claims, and mayinclude other examples that occur to those skilled in the art. Suchother examples are intended to be within the scope of the claims if theyhave structural elements that do not differ from the literal language ofthe claims, or if they include equivalent structural elements withinsubstantial differences from the literal languages of the claims.Aspects from the various embodiments described, as well as other knownequivalents for each such aspects, can be mixed and matched by one ofordinary skill in the art to construct additional embodiments andtechniques in accordance with principles of this application.

Those in the art will appreciate that various adaptations andmodifications of the above-described embodiments can be configuredwithout departing from the scope and spirit of the claims. Therefore, itis to be understood that the claims may be practiced other than asspecifically described herein.

1. A system comprising: an installed product, including a plurality ofcomponents; a computer programmed with a damage metric model for theinstalled product, the damage metric model for providing an estimate ofan extent of damage on one or more components; the computer programmedwith a dynamic process control model for providing a dynamic response ofthe installed product with respect to its one or more operatingparameters; the computer further programmed with a true-up model forproviding a control action to reduce an uncertainty of the estimateprovided by the damage metric model; the computer including a processorand a memory in communication with the processor, the memory storing thedamage metric model and the true-up model; the memory storing additionalprogram instructions, the processor operative with the additionalprogram instructions to perform functions as follows: receiving anestimate output of the damage metric model, wherein the output includesthe estimate of the extent of damage on the one or more components;generating, via the dynamic process control model, an operating responseof the installed product to the received estimate output; in response toreceipt of the estimate output, executing the true-up model; andgenerating, via execution of the true-up model, the plan to reduceuncertainty of the estimate output.
 2. The system of claim 1, whereinthe computer is programmed with a performance metric model forgenerating an estimate of system-level operating performance on at leastone of one or more asset and one or more coupled systems; and whereinthe computer is further programmed with the true-up model for generatingthe plan to reduce an uncertainty of the estimate of system-leveloperating performance provided by the performance metric model.
 3. Thesystem of claim 1, wherein the control action is an automatedperturbation of the installed product or a specific physicalmeasurement.
 4. The system of claim 1, wherein the processor is furtheroperative with the additional program instructions to perform functionsas follows: executing the control action.
 5. The system of claim 1,wherein the control action includes at least one of an inspection of theone or more components, execution of an in-operation test sequenceassociated with the one or more components, and execution of anin-operation data burst associated with the one or more components. 6.The system of claim 1, wherein a sequence and use of true-up modalitiesare computed to maximize a specificity required for operational controlat a desired confidence interval at one or more intervals of time. 7.The system of claim 1, wherein the estimate of the extent of damage isfor one or more sub-components of the one or more components.
 8. Thesystem of claim 1, wherein the estimate of the extent of damage is forone or more assets in a process or system.
 9. The system of claim 1,wherein the processor is further operative with the additional programinstructions to perform functions as follows: in response to receipt ofthe estimate output, determining the uncertainty associated with theestimate output; and determining the determined uncertainty is too highprior to executing the true-up model.
 10. The system of claim 7, whereinthe processor is further operative with the additional programinstructions to perform functions as follows: determining a firstuncertainty associated with the estimate output for each of the one ormore sub-components; and identifying one or more estimate outputs toreduce uncertainty via execution of the true-up model.
 11. The system ofclaim 10, wherein the processor is further operative with the additionalprogram instructions to perform functions as follows: quantifying anamount of uncertainty reduction for each of the identified one or moreestimate outputs.
 12. The system of claim 11, wherein execution of thecontrol action further comprises, generating at least one of aninspection method, an in-operation test sequence method and anin-operation data burst method; and determining a second uncertaintyassociated with each of the inspection method, the in-operation testsequence method and the in-operation data burst method.
 13. The systemof claim 12, wherein generating the control action to reduce uncertaintyof the estimate output is based on the determined second uncertainty.14. The system of claim 9, wherein determining the determineduncertainty is too high is based on a non-damage related metric.
 15. Thesystem of claim 14, wherein the determined uncertainty is based on anaggregate of two or more uncertainties, each associated with theestimate of the extent of damage associated with the component.
 16. Thesystem of claim 15, further comprising: determining an effect of each oftwo or more uncertainties on the aggregate uncertainty.
 17. The systemof claim 16, further comprising: based on a determination that a firstone of the two or more uncertainties affects the aggregate uncertaintymore than at least a second one of the two or more uncertainties,executing the true-up model to true-up the estimate output associatedwith the first uncertainty.
 18. A method comprising: receiving, at adamage metric model, state data associated with one or more componentsof an installed product; generating, via the damage metric model, anestimate output of an extent of damage on the one or more components;receiving the estimate output at a damage module; generating, via adynamic process control model, an operating response of the installedproduct to the received estimate output; and in response to receivingthe estimate output, executing a true-up model to generate a controlaction to reduce uncertainty of the estimate output.
 19. The method ofclaim 18, further comprising: generating an estimate of system-leveloperating performance on at least one of one or more asset and one ormore coupled systems via a performance metric model; and generating thecontrol action, via the true-up model, to reduce an uncertainty of theestimate of system-level operating performance provided by theperformance model.
 20. The method of claim 18, wherein the controlaction is an automated perturbation of the installed product or aspecific physical measurement.